convergents_sequence

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does without any details on behavior, such as output format (e.g., list of rationals), error handling (e.g., for invalid input), computational limits, or side effects. This leaves the agent with insufficient information to predict how the tool behaves beyond its basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded: a single sentence that directly states the tool's purpose, followed by domain and category tags. There is no wasted verbiage or redundancy, making it efficient for quick comprehension. The structure is clear and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (mathematical computation with 1 parameter), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It fails to address key contextual elements: what the output looks like (sequence format), any behavioral traits (e.g., handling of edge cases), or parameter details. The description alone is insufficient for an agent to use the tool effectively without additional assumptions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 parameter ('cf') with 0% description coverage, meaning the schema provides no semantic information. The description does not compensate by explaining what 'cf' represents (e.g., an array of integers defining the continued fraction coefficients), its constraints (e.g., non-negative, finite length), or examples. This leaves the parameter's meaning and usage unclear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate sequence of convergents for a continued fraction.' It specifies the verb ('generate'), resource ('sequence of convergents'), and domain context ('continued fraction'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'cf_convergence_analysis' or 'convergent_properties', which could handle related aspects of convergents, so it falls short of a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions the domain ('arithmetic') and category ('continued_fractions'), but this is too vague to inform selection among the many sibling tools in the same domain, such as 'cf_convergence_analysis' or 'convergent_properties'. There are no explicit instructions on use cases, prerequisites, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.